Detecting faces in images: A survey

Ming Hsuan Yang, David J. Kriegman, Narendra Ahuja

Research output: Contribution to journalArticlepeer-review

2530 Citations (Scopus)

Abstract

Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its three-dimensional position, orientation, and lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

Original languageEnglish
Pages (from-to)34-58
Number of pages25
JournalIEEE transactions on pattern analysis and machine intelligence
Volume24
Issue number1
DOIs
Publication statusPublished - 2002 Jan

Bibliographical note

Funding Information:
The authors would like to thank Kevin Bowyer and the anonymous reviewers for their comments and suggestions. The authors also thank Baback Moghaddam, Henry Rowley, Brian Scassellati, Henry Schneiderman, Kah-Kay Sung, and Kin Choong Yow for providing images. M.-H. Yang was supported by ONR grant N00014-00-1-009 and Ray Ozzie Fellowship. D. J. Kriegman was supported in part by US National Science Foundation ITR CCR 00-86094 and the National Institute of Health R01-EY 12691-01. N. Ahuja was supported in part by US Office of Naval Research grant N00014-00-1-009.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Detecting faces in images: A survey'. Together they form a unique fingerprint.

Cite this